Information Technology Reference
In-Depth Information
10.3.7 Computer Architectures for Evolvable Hardware
Another promising technology is reconfigurable hardware that evolves in a way to
best solve the problem at hand. Evolvable hardware exploits programmable circuit
devices such as
field programmable gate arrays
(FPGAs). These are integrated cir-
cuit chips with a large number of simple logic units or
gates
. Settable switches
called
architecture bits
or
configuration memory
program the logical function and
interconnection of these gates. Field programmable gate arrays allow the mass man-
ufacture of standardised silicon that has its circuit-level functionality postponed for
later definition. This circuit-level functionality is lower and faster than that achieved
by executing machine language code (Yao and Higuchi
1997
).
By treating the architecture bits as a chromosome the configuration of field pro-
grammable gate arrays can be determined using evolutionary methods. Evolution in
this case doesn't design the gate array configuration so much as it designs the chip's
behaviour relative to some fitness function defined need. In this some see a paral-
lel to the way neurones exhibit emergent learning. And because these chips can be
reprogrammed on the fly there is the possibility of learning adaptation to changing
conditions.
It's worth noting that a proposed evolvable hardware system has been simulated
in software, and used as a pattern recognition system for facial recognition with an
experimental accuracy of 96.25 % (Glette et al.
2007
).
10.4 Conclusion
Computational aesthetic evaluation victories have been few and far between. The
successful applications have mostly been narrowly focused point solutions. Negative
experience to date with low dimensional models such as formulaic and geometric
theories makes success with similar approaches in the future quite unlikely.
Evolutionary methods, including those with extensions such as coevolution,
niche construction, and agent swarm behaviour and curiosity, have had some cir-
cumscribed success. The noted extensions have allowed evolutionary art to iterate
many generations quickly by eliminating the need for interactive fitness evaluation.
They have also allowed researchers to gain insight into how aesthetic values can
be created as emergent properties. In such explorations, however, the emergent ar-
tificial aesthetics themselves seem alien and unrelated to human notions of beauty.
They have not yet provided practical leverage when the goal is to model, simulate,
or predict human aesthetics via machine evaluation.
I've suggested that a paradigm like effective complexity may be more useful than
information or algorithmic complexity when thinking about aesthetics. Effective
complexity comes with the notion of balancing order and disorder “built in”, and
that balance is critical in all forms of aesthetic perception and the arts.
There is also a plausible evolutionary hypothesis for suggesting that effective
complexity correlates well with aesthetic value. Effective complexity is maximised
